Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Reactivity-based metabolomics reveal cysteine has glyoxalase 1-like and glyoxalase 2-like activities

Abstract

Methylglyoxal (MG) is a reactive metabolite involved in diabetes and aging through the formation of protein adducts. Less is known about the extent that MG and its metabolic product S-d-lactoylglutathione (LGSH) form adducts with cell metabolites. Using a ‘symmetric’ isotope-labeled and reactivity-based metabolomics approach in living cells, we found over 200 adducts and, surprisingly, discovered that 10 of the most abundant are lactoylated amino acids mainly derived from LGSH. The most abundant adduct d-Lac-Cys is formed rapidly between LGSH and cysteine, whereas the diastereoisomer l-Lac-Cys is formed directly from MG and cysteine, assigning cysteine with both glyoxalase 1-like and glyoxalase 2-like activity. Cellular cysteine and MG dynamically regulate d-Lac-Cys and l-Lac-Cys levels and the adducts are increased in diabetes, suggesting their use as novel biomarkers. Lastly, cysteine amides, as proxies for protein cysteines, also undergo lactoylation by MG and LGSH, suggesting the existence of two additional pathways for nonenzymatic lactoylation of proteins.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Metabolic origin of MG and its derived protein adducts and metabolites.
Fig. 2: Reactivity-based metabolomics reveals lactoylated amino acids as prominent MG-derived and LGSH-derived metabolites in HEK293 WT cells.
Fig. 3: GLO2 is a major regulator of d-lactoyl amino acid formation.
Fig. 4: Cellular cysteine scavenges LGSH and MG to regulate Lac-Cys.
Fig. 5: d-Lac-Cys and l-Lac-Cys are increasingly excreted in streptozotocin-induced diabetes.
Fig. 6: Cysteine has GLO1-like and GLO2-like activity in cells.

Similar content being viewed by others

Data availability

The metabolomics datasets generated during the current study were deposited to the MetaboLight repository with dataset identifier MTBLS6287. All other datasets generated and analyzed during the current study are presented in the manuscript or Supplementary Information. Source data are provided with this paper.

References

  1. Kold-Christensen, R. & Johannsen, M. Methylglyoxal metabolism and aging-related disease: moving from correlation toward causation. Trends Endocrinol. Metab. 31, 81–92 (2020).

    Article  CAS  PubMed  Google Scholar 

  2. Gaffney, D. O. et al. Non-enzymatic lysine lactoylation of glycolytic enzymes. Cell Chem. Biol. 27, 206–213 (2020).

    Article  CAS  PubMed  Google Scholar 

  3. Lamprea-Montealegre, J. A. et al. Plasma levels of advanced glycation endproducts and risk of cardiovascular events: findings from 2 prospective cohorts. J. Am. Heart Assoc. 11, e024012 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Coukos, J. S. & Moellering, R. E. Methylglyoxal forms diverse mercaptomethylimidazole crosslinks with thiol and guanidine pairs in endogenous metabolites and proteins. ACS Chem. Biol. 16, 2453–2461 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Salomon, T. et al. Ketone body acetoacetate buffers methylglyoxal via a non-enzymatic conversion during diabetic and dietary ketosis. Cell Chem. Biol. 24, 935–943 (2017).

    Article  CAS  PubMed  Google Scholar 

  6. Corbett, A. J. et al. T-cell activation by transitory neo-antigens derived from distinct microbial pathways. Nature 509, 361–365 (2014).

    Article  CAS  PubMed  Google Scholar 

  7. Li, V. L. et al. An exercise-inducible metabolite that suppresses feeding and obesity. Nature 606, 785–790 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Nemet, I. et al. A cardiovascular disease-linked gut microbial metabolite acts via adrenergic receptors. Cell 180, 862–877 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Mossad, O. et al. Gut microbiota drives age-related oxidative stress and mitochondrial damage in microglia via the metabolite N6-carboxymethyllysine. Nat. Neurosci. 25, 295–305 (2022).

    Article  CAS  PubMed  Google Scholar 

  10. Sharma, R. et al. Circulating markers of NADH-reductive stress correlate with mitochondrial disease severity. J. Clin. Invest. 131, e136055 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Poulsen, M. W. et al. Advanced glycation endproducts in food and their effects on health. Food Chem. Toxicol. 60, 10–37 (2013).

    Article  CAS  PubMed  Google Scholar 

  12. Sibbersen, C. & Johannsen, M. Dicarbonyl derived post-translational modifications: chemistry bridging biology and aging-related disease. Essays Biochem. 64, 97–110 (2020).

    Article  CAS  PubMed  Google Scholar 

  13. Hacker, S. M. et al. Global profiling of lysine reactivity and ligandability in the human proteome. Nat. Chem. 9, 1181–1190 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Weerapana, E. et al. Quantitative reactivity profiling predicts functional cysteines in proteomes. Nature 468, 790–795 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lin, W., Conway, L. P., Vujasinovic, M., Löhr, J. M. & Globisch, D. Chemoselective and highly sensitive quantification of gut microbiome and human metabolites. Angew. Chem. Int. Ed. Engl. 60, 23232–23240 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Aćimović, J. M., Stanimirović, B. D., Todorović, N., Jovanović, V. B. & Mandić, L. M. Influence of the microenvironment of thiol groups in low molecular mass thiols and serum albumin on the reaction with methylglyoxal. Chem. Biol. Interact. 188, 21–30 (2010).

    Article  PubMed  Google Scholar 

  17. Edwards, L. G. & Thornalley, P. J. Prevention of S-d-lactoylglutathione-induced inhibition of human leukaemia 60 cell growth by uridine. Leuk. Res. 18, 717–722 (1994).

    Article  CAS  PubMed  Google Scholar 

  18. Edwards, L. G., Adesida, A. & Thornalley, P. J. Inhibition of human leukaemia 60 cell growth by S-d-lactoylglutathione in vitro. Mediation by metabolism to N-d-lactoylcysteine and induction of apoptosis. Leuk. Res. 20, 17–26 (1996).

    Article  CAS  PubMed  Google Scholar 

  19. Sumner, L. W. et al. Proposed minimum reporting standards for chemical analysis. Metabolomics 3, 211–221 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Stipanuk, M. H., Dominy, J. E., Lee, J.-I. & Coloso, R. M. Mammalian cysteine metabolism: new insights into regulation of cysteine metabolism. J. Nutr. 136, 1652S–1659S (2006).

    Article  CAS  PubMed  Google Scholar 

  21. Rabbani, N. & Thornalley, P. J. Measurement of methylglyoxal by stable isotopic dilution analysis LC–MS/MS with corroborative prediction in physiological samples. Nat. Protoc. 9, 1969–1979 (2014).

    Article  CAS  PubMed  Google Scholar 

  22. Hall, S. S., Doweyko, A. M. & Jordan, F. Glyoxalase I enzyme studies. 4. General base catalyzed enediol proton transfer rearrangement of methyl- and phenylglyoxalglutathionylhemithiol acetal to S-lactoyl- and S-mandeloylglutathione followed by hydrolysis. A model for the glyoxalase enzyme system. J. Am. Chem. Soc. 100, 5934–5939 (1978).

    Article  CAS  Google Scholar 

  23. Weber, A. L. Formation of the thioester, N-acetyl, S-lactoylcysteine, by reaction of N-acetylcysteine with pyruvaldehyde in aqueous solution. J. Mol. Evol. 18, 354–359 (1982).

    Article  CAS  PubMed  Google Scholar 

  24. Dawson, P. E., Muir, T. W., Clark-Lewis, I. & Kent, S. B. H. Synthesis of proteins by native chemical ligation. Science 266, 776–779 (1994).

    Article  CAS  PubMed  Google Scholar 

  25. Burke, H. M., McSweeney, L. & Scanlan, E. M. Exploring chemoselective S-to-N acyl transfer reactions in synthesis and chemical biology. Nat. Commun. 8, 15655 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  26. Thornalley, P. J., Yurek-George, A. & Argirov, O. K. Kinetics and mechanism of the reaction of aminoguanidine with the α-oxoaldehydes glyoxal, methylglyoxal, and 3-deoxyglucosone under physiological conditions. Biochem. Pharmacol. 60, 55–65 (2000).

    Article  CAS  PubMed  Google Scholar 

  27. Coukos, J. S., Lee, C. W., Pillai, K. S., Shah, H. & Moellering, R. E. PARK7 catalyzes stereospecific detoxification of methylglyoxal consistent with glyoxalase and not deglycase function. Biochemistry 62, 3126–3133 (2023).

    Article  CAS  PubMed  Google Scholar 

  28. Jansen, R. S. et al. N-lactoyl-amino acids are ubiquitous metabolites that originate from CNDP2-mediated reverse proteolysis of lactate and amino acids. Proc. Natl Acad. Sci. USA 112, 6601–6606 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Adesida, A., Edwards, L. G. & Thornalley, P. J. Facile synthesis of (R)N-2-hydroxyacyl-l-cysteine derivatives: (R)N-2-hydroxyacyl transfer from enzymatically-synthesized (R)S-2-hydroxyacylglutathione derivatives to l-cysteine. Amino Acids 9, 185–189 (1995).

    Article  CAS  PubMed  Google Scholar 

  30. Bracher, P. J., Snyder, P. W., Bohall, B. R. & Whitesides, G. M. The relative rates of thiol–thioester exchange and hydrolysis for alkyl and aryl thioalkanoates in water. Orig. Life Evol. Biosph. 41, 399–412 (2011).

    Article  CAS  PubMed  Google Scholar 

  31. Bizzozero, O. A., Bixler, H. A. & Pastuszyn, A. Structural determinants influencing the reaction of cysteine-containing peptides with palmitoyl-coenzyme A and other thioesters. Biochim. Biophys. Acta 1545, 278–288 (2001).

    Article  CAS  PubMed  Google Scholar 

  32. Saito, F., Noda, H. & Bode, J. W. Critical evaluation and rate constants of chemoselective ligation reactions for stoichiometric conjugations in water. ACS Chem. Biol. 10, 1026–1033 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Banjac, A. et al. The cystine/cysteine cycle: a redox cycle regulating susceptibility versus resistance to cell death. Oncogene 27, 1618–1628 (2007).

    Article  PubMed  Google Scholar 

  34. Sibbersen, C. et al. Profiling of methylglyoxal blood metabolism and advanced glycation end-product proteome using a chemical probe. ACS Chem. Biol. 13, 3294–3305 (2018).

    Article  CAS  PubMed  Google Scholar 

  35. Kanikarla-Marie, P., Micinski, D. & Jain, S. K. Hyperglycemia (high-glucose) decreases l-cysteine and glutathione levels in cultured monocytes and blood of Zucker diabetic rats. Mol. Cell. Biochem. 459, 151–156 (2019).

    Article  CAS  PubMed  Google Scholar 

  36. He, Y. et al. Glyoxalase system: a systematic review of its biological activity, related-diseases, screening methods and small molecule regulators. Biomed. Pharmacother. 131, 110663 (2020).

    Article  CAS  PubMed  Google Scholar 

  37. Luengo, A. et al. Reactive metabolite production is a targetable liability of glycolytic metabolism in lung cancer. Nat. Commun. 10, 5604–5604 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Sutton, T. R. et al. A robust and versatile mass spectrometry platform for comprehensive assessment of the thiol redox metabolome. Redox Biol. 16, 359–380 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhao, S. & Li, L. Chemical derivatization in LC–MS-based metabolomics study. TrAC, Trends Anal. Chem. 131, 115988 (2020).

    Article  CAS  Google Scholar 

  40. Chen, Y. J. et al. Lactate metabolism is associated with mammalian mitochondria. Nat. Chem. Biol. 12, 937–943 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. James, A. M. et al. The causes and consequences of nonenzymatic protein acylation. Trends Biochem. Sci. 43, 921–932 (2018).

    Article  CAS  PubMed  Google Scholar 

  42. Akhmadi, A. et al. DJ-1 protects proteins from acylation by catalyzing the hydrolysis of highly reactive cyclic 3-phosphoglyceric anhydride. Nat. Commun. 15, 2004 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Ko, Y. et al. S-lactoyl modification of KEAP1 by a reactive glycolytic metabolite activates NRF2 signaling. Proc. Natl Acad. Sci. Usa. 120, e2300763120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Bollong, M. J. et al. A metabolite-derived protein modification integrates glycolysis with KEAP1–NRF2 signalling. Nature 562, 600–604 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Coukos, J. S., Lee, C. W., Pillai, K. S., Liu, K. J. & Moellering, R. E. Widespread, reversible cysteine modification by methylglyoxal regulates metabolic enzyme function. ACS Chem. Biol. 18, 91–101 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Parvez, S., Long, M. J. C., Poganik, J. R. & Aye, Y. Redox signaling by reactive electrophiles and oxidants. Chem. Rev. 118, 8798–8888 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Sánchez-Gómez, F. J. et al. Detoxifying enzymes at the cross-roads of inflammation, oxidative stress, and drug hypersensitivity: role of glutathione transferase P1-1 and aldose reductase. Front. Pharmacol. 7, 237 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  48. Farrera, D. O. & Galligan, J. J. The human glyoxalase gene family in health and disease. Chem. Res. Toxicol. 35, 1766–1776 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Sibbersen, C. et al. Development of a chemical probe for identifying protein targets of α-oxoaldehydes. Chem. Commun. 49, 4012 (2013).

    Article  CAS  Google Scholar 

  50. Brauch, S. et al. Fast and efficient MCR-based synthesis of clickable rhodamine tags for protein profiling. Org. Biomol. Chem. 10, 958–965 (2012).

    Article  CAS  PubMed  Google Scholar 

  51. Smith, C. A., Want, E. J., O’Maille, G., Abagyan, R. & Siuzdak, G. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal. Chem. 78, 779–787 (2006).

    Article  CAS  PubMed  Google Scholar 

  52. Berthold, M. R. et al. KNIME: the Konstanz information miner. In Data Analysis, Machine Learning and Applications (eds Preisach, C., Burkhardt, H., Schmidt-Thieme, L. & Decker, R.) (Springer, 2008).

  53. Tautenhahn, R., Böttcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS. BMC Bioinformatics 9, 504 (2008).

    Article  PubMed  PubMed Central  Google Scholar 

  54. Prince, J. T. & Marcotte, E. M. Chromatographic alignment of ESI-LC–MS proteomics data sets by ordered bijective interpolated warping. Anal. Chem. 78, 6140–6152 (2006).

    Article  CAS  PubMed  Google Scholar 

  55. Kuhl, C., Tautenhahn, R., Böttcher, C., Larson, T. R. & Neumann, S. CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets. Anal. Chem. 84, 283–289 (2012).

    Article  CAS  PubMed  Google Scholar 

  56. Tesch, G. H. & Allen, T. J. Rodent models of streptozotocin‐induced diabetic nephropathy (methods in renal research). Nephrology 12, 261–266 (2007).

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was generously supported by Ingeborg og Leo Dannins Legat (J.nr. 10017-1, to M.J.), the Novo Nordisk Foundation (NNF20OC0065548, to M.J.), the Graduate School of Health, Aarhus University (to M.D.O.) and the Danish Diabetes Academy (PhD001-19, to S.B.O.). This project received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation program (grant agreement 865738, to T.B.P.). Financial support was provided by National Institutes of Health grants: R35 GM137910 and R01 DK133196 to J.J.G.

Author information

Authors and Affiliations

Authors

Contributions

M.J. conceptualized and, together with J.H., supervised the study. M.D.O. and M.B.S. performed the reactivity-based metabolomics experiments. C.B.N. performed preprocessing of the metabolomics data and T.W. assisted in the identification of isotopic pairs. C.B.N., M.D.O., M.B.S. and L.V.B. performed the feature identification and MS2 fragmentations. J.H. and M.B.S. designed, performed and analyzed the cystine-based cell experiments. J.H. and M.B.S. performed the chemoproteomic experiment and analyzed the data. M.D.O. and L.V.B. performed and analyzed the in vitro experiments with isolated amino acids and LGSH or MG. L.V.B. and J.H. performed the kinetic analysis involving LGSH, AcSCoA, MG and cysteine and D.B. performed the computational analysis of the reaction rate for the GLO1-like reaction. C.B.H. designed and performed the in vivo study with the diabetes mouse model and carried out the creatinine and blood glucose measurements under the supervision of J.A.Ø. M.B.S. and C.B.N. performed the urine sample extraction and MRM-based analysis. M.B.S. and K.F. synthesized the lactoylated amino acids, S.B.O. synthesized the isotopically labeled MG and A.M synthesized the d-Lac-Cys and l-Lac-Cys dimers under the supervision of T.B.P. J.M.S. performed the mouse d-Lac experiment under the supervision of R.R.N. J.J.G. generated the GLO2 KO cell line. K.L.N. conducted the initial analysis to identify the lactoylated amino acids. M.J., in collaboration with M.D.O., J.H. and M.B.S., wrote the manuscript and prepared the figures with contributions from T.B.P. and input from all other authors.

Corresponding author

Correspondence to Mogens Johannsen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Chemical Biology thanks Rebecca Scheck and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Volcano plot of reactivity-based metabolomics data in positive ionization mode.

Volcano plot comparing the ‘light’ (12C3) and ‘heavy’ (13C3) treatment groups from the experiment depicted in Fig. 2a in ESI+ mode (n = 5)(left). The y-axis represents the log10-transformed p-values calculated by an unpaired t-test. The x-axis shows the log10-transformed feature ratio 12C3-MG sample/13C3-MG sample. MG-pairs were considered significant at a ratio cutoff of <0.8 or >1.2 and a p-value < 0.05 (unpaired t-test; equal, or unequal variance based on an F-test; p-values provided in source data). The numbers indicate matching pairs of identified features. Pie chart showing MG pairs discovered in both ionization modes (yellow), pairs selectively found in positive ionization mode (ESI+; red) and pairs selectively found in negative ionization mode (ESI;blue)(right).

Source data

Extended Data Fig. 2 Glyoxalase 1 like mechanism of formation of D- and L-Lac-Cys from MG and cysteine.

(a) Extracted ion chromatogram (EIC) of D-Lac-Cys, L-Lac-Cys and of in vitro reaction mixture of 50 μM MG and 150 μM cysteine (24h) demonstrating that D-Lac-Cys and L-Lac-Cys are formed directly from MG and cysteine (MG + Cys)(left). MS/MS spectra of peaks corresponding to D-Lac-Cys and L-Lac-Cys in reaction mixture (right). MS/MS spectra can be compared to MS/MS spectra of D-Lac-Cys and L-Lac-Cys in Fig. 2. (b) Methylglyoxal (MG, 1.3%) is in a fast equilibrium with a monohydrate (MG(H2O) 28.8%), dihydrate (MG(H2O)2, 69.9%) and a hemithioacetal (HTA, 0.2%) when mixed with 150 μM cysteine in an aqueous buffer. After mixing, HTA rearranges into initially S,D/L-Lac-Cys by a glyoxalase 1 like rearrangement followed by a S-to-N lactoyl transfer to finally form D- and L-Lac-Cys in a ratio of 1:2 and with a first order rate constant k of 10−3-10−4 s−1. For details of computational calculations see Supplementary Note 1.

Extended Data Fig. 3 Cysteine amides undergo glyoxalase 1 reactions to form hydrolytically instable thioesters.

(a) Methylglyoxal (MG, 80 µM) incubated with glutathione (GSH, 0.15 or 2 mM) in PBS at 37 °C and the non-enzymatic formation of S,D-lactoylglutathione (LGSH) was followed in a 24-hour period. The Glo1 enzyme catalyzed reaction was investigated by quantifying LGSH before and after adding Glo1 (10 µM) to a reaction between MG (80 µM) and GSH (2 mM) in PBS at 37 °C. (b) The stability of LGSH in PBS at 37 °C. (c) Lactate formation from LGSH in the reactions described in (a and b). (d) Lactate formation from MG (2 mM) in PBS or H2O at 37 °C. The effects of adding cysteine (Cys, 0.2 or 2 mM) or N-acetylcysteine (NAC, 0.2 or 2 mM) to MG in PBS was studied in additional reactions. All reactions were performed with three replicates and the mean values +/− SD for all timepoints are shown. LGSH and lactate were quantified by LC-MS/MS as described in the methods and material section.

Source data

Extended Data Fig. 4 Preferential formation of D-lactoylated metabolites in GLO2 KO cells.

(a) Immunoblot of GLO2 and GAPDH in WT and GLO2 KO cells. Shown are protein data from 3 independent cultures of each cell type analyzed on the same blot. (b) Volcano plot comparing the ‘light’ (12C3) and ‘heavy’ (13C3) treatment groups from the experiment depicted in Fig. 3 in ESI+ mode (n = 5)(left). The y-axis represents the log10-transformed p-values calculated by an unpaired t-test. The x-axis shows the log10-transformed feature ratio 12C3-MG sample/13C3-MG sample. MG-pairs were considered significant at a ratio cutoff of <0.8 or >1.2 and a p-value <0.05 (unpaired t-test; equal, or unequal variance based on an F-test; p-values provided in source data). Pale colored circles (red and blue) show matching MG-pairs (20) also observed in the WT experiment (see Fig. 2a,b). Darker colored circles (red and blue) indicate additional MG pairs observed in GLO2 KO cells (112). Pie chart showing MG pairs discovered in both ionization modes (yellow), pairs selectively found in positive ionization mode (ESI+; red) and pairs selectively found in negative ionization mode (ESI ;blue)(right).

Source data

Extended Data Fig. 5 LGSH trans-acylates around 200 times faster than AcASCoA with cysteine as acceptor.

(a) Yield of D-Lac-Cys and elimination of LGSH monitored as function of time for the reaction between 1 µM LGSH and 50-250 µM cysteine (Cys) under simulated physiological conditions (top). Yield of D-Lac-Cys and elimination of LGSH from a single data series using 1 µM LGSH and 100 µM cysteine (Cys)(bottom left). Elimination of LGSH at the five different levels of cysteine (bottom middle). Rate of reaction (k = 10 M−1S−1) determined as slope of curve from plot of observed rate constants (k’) at the five different cysteine concentrations (M)(bottom right). All reaction time courses were performed three independent times and mean values ± SD (n = 3) are shown. See method section and source data for further details and calculations. (b) Yield of N-acetyl cysteine (NAC) monitored as function of time for the reaction between 10 µM AcSCoA and 50-250 µM cysteine (Cys) under simulated physiological conditions (top). Yield of NAC as function of time for a single data series using 10 µM AcSCoA and 50–100 µM cysteine (bottom left). Formation of NAC over time at the five levels of cysteine (bottom middle). Rate of reaction (k = 0.05 M−1 S−1) determined as slope of curve from plot of observed rate constants (k’) at the four different cysteine concentrations (M)(bottom right). All reaction time courses were performed three independent times and mean values ± SD (n = 3) are shown. See method section for further experimental details and source data for further calculations.

Source data

Extended Data Fig. 6 Cysteine impact on AGEs (MG-H1 and CEA) and Lac-Cys kinetics in cells.

(a) Relative levels of reduced glutathione (GSH) by LC-MS/MS after cystine (Cys2) pre-conditioning (1, 2.5 mM or vehicle for 6h) in WT and GLO2 KO cells. Mean values ± SD (n = 4) are shown. P-values for WT cells (P = 0.2 for 1 mM Cys vs vehicle and P = 0.5 for 2.5 mM vs vehicle), and for GLO2 KO (P = 0.23 and P = 0.56). (b) MG-H1 and (c) CEA levels after exhaustive enzymatic hydrolysis of protein material from cells challenged with MG (0.5 mM for 6 hours) following a 6-hour pre-conditioning period in cystine. P-values for testing MG-H1 levels (b) in cystine vs vehicle (WT: p = 0.7 (1 mM), p = 0.56 (2.5 mM), and for GLO2 KO: p = 0.02 (1 mM), p = 0.09 (2.5 mM). P-values for testing CEA MG-H1 levels (c) in cystine vs vehicle (WT: p = 0.75 (1 mM), p = 0.92 (2.5 mM), and for GLO2 KO: p = 0.22 (1 mM), p = 0.14 (2.5 mM). Data shown in (b-c) are means ± SD from 5 cell cultures (n = 5). Test of means (a-c) based on a one-way ANOVA using a Dunnet´s post-hoc test; ns p > 0.05, *p < 0.05. (d) and (e) Kinetics of formation (24-hour period) of stable isotope labelled D-Lac-Cys (d) and L-Lac-Cys (e) after a pulse of 0.5 mM 13C3-MG in WT and GLO2 KO cells. Data from two pulse cell experiments shown fitted with a line.

Source data

Extended Data Fig. 7 Abundance of L- and D-Lac-AAs in cells and human plasma.

(a) Basal levels of MG/LGSH and L-lactate derived metabolites in different human cell lines. The lactoylated metabolites were quantified in lysates generated from the same number of cells from each cell line, data are presented as mean values +/− SD from 5 cell cultures (n = 5). (b and c) LC-MS/MS measurements and chromatograms from three different MRM transitions of D-Lac-Phe (b) and L-Lac-Phe (c) authentic standards (top left) and their basal levels in four human plasma samples. See also Supplementary Fig. 7.

Source data

Extended Data Fig. 8

Cysteine amides react with MG and LGSH to generate Lac-Cys or lactate in GLO1 and 2 like reactions.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8, Notes 1–3 and Tables 1–3.

Reporting Summary

Supplementary Data 1

Source data for Supplementary Fig. 5.

Supplementary Data 2

Source data for Supplementary Fig. 6.

Supplementary Data 3

Source data for Supplementary Note 3.

Supplementary Data 4

Source data for Supplementary Note 2 and Supplementary Fig. 2.

Supplementary Data 5

Source data for Supplementary Note 2 and Supplementary Fig. 1.

Source data

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 1

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Source Data Extended Data Fig. 6

Statistical source data.

Source Data Extended Data Fig. 7

Statistical source data.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Daniel Opfermann, M., Bøgelund Søndergård, M., Vase Bech, L. et al. Reactivity-based metabolomics reveal cysteine has glyoxalase 1-like and glyoxalase 2-like activities. Nat Chem Biol (2025). https://doi.org/10.1038/s41589-025-01909-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41589-025-01909-0

Search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research